Negative Selection Algorithm (NSA) is a bio-inspired approach and a powerful computational tool for some applications where the decision lies in the complementary space of positive profile (data). Existing works showed that NSAs are beneficial for one class classifications, outlier detection, fault and intrusion detection problems. Inspired by the human immune system, NSA variations emerged with different representation, distance measures, coverage estimations for different applications over last three decades. We briefly reviewed these and compared NSA with other ML models such as neural network-based techniques, discussed their advantages and limitations with directions in future research. NSA method is most compared with one class support vector machine (OCSVM). OCSVM[1]} and NSA both can work with one class of data. But NSA can work with high dimension data where OCSVM performance fail when data start to became non linear. Some similar other known approaches are Isolation forest (IF) [2]}, Local outlier factors (LOF) [3]}, Minimum Co-variance Method (MCM) [4]}, Gaussian Mixture Model (GMM)[5]}, Dirichlet Process Mixture Model (DPMM)[6]}, Kernel Density Estimator (KDE)[7]}, Robust KDE[8]}. GWR-Netwrok[9]}, Deep Support Vector Data Description (SVDD), Angle based Outlier Detection (ABOD)[10]}, Subspace Outlier Detection (SOD) [11]}, Deep Auto Encoder based Methods[12]}, Generative Adversarial Net Based approaches (eg: [13]}, [14]}. In this paper, we compared these methods with NSA approaches for outlier detection data-sets and also used a synthetic data set to visualize the comparison.
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